PhD Chapter 1

Global - Analyses and regressions

This series of files compile analyses done for the global analysis of Chapter 1 (version of May 15th).

All analyses have been done with PRIMER-e 6 and R 4.1.0.

Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it

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We used data from subtidal ecosystems (see metadata files for more information). Only stations that have been sampled both for abiotic parameters and benthic species were included.

Selected variables for the analyses:

1. Cluster characteristics

This section present each clusters, with (i) the average values for habitat and metal variables, (ii) the average values for diversity indices and (iii) the characteristic taxa obtained by SIMPER and IndVal analyses. Taxon densities were (log+1) transformed.

0.5 mm community
Habitat and metals
Cluster 1
  Mean SD SE Median Min Max 95% CI
depth 6.994 1.403 0.351 7.100 4.500 8.900 0.687
om 2.584 1.660 0.415 2.378 1.108 8.260 0.813
gravel 0.000 0.000 0.000 0.000 0.000 0.000 0.000
sand 0.000 0.000 0.000 0.000 0.000 0.000 0.000
silt 0.001 0.000 0.000 0.001 0.001 0.001 0.000
clay 0.999 0.000 0.000 0.999 0.999 0.999 0.000
arsenic 3.744 1.293 0.323 3.750 1.50 6.00 0.633
cadmium 0.146 0.027 0.007 0.140 0.09 0.19 0.013
chromium 80.287 22.934 5.734 79.450 45.80 143.30 11.238
copper 19.887 5.319 1.330 19.600 11.20 32.40 2.606
iron 64730.012 14916.663 3729.166 64356.210 32899.91 98544.60 7309.030
manganese 2173.444 1284.808 321.202 2048.495 705.69 5962.19 629.544
mercury 0.036 0.063 0.016 0.020 0.00 0.25 0.031
lead 7.316 2.312 0.578 7.115 3.59 12.18 1.133
zinc 77.213 16.879 4.220 74.900 47.10 101.50 8.270
Cluster 2
  Mean SD SE Median Min Max 95% CI
depth 13.720 12.804 1.639 8.800 1.600 60.600 3.213
om 0.524 0.337 0.043 0.409 0.186 1.528 0.084
gravel 0.059 0.142 0.018 0.000 0.000 0.701 0.036
sand 0.533 0.415 0.053 0.675 0.000 1.000 0.104
silt 0.113 0.187 0.024 0.032 0.000 0.869 0.047
clay 0.295 0.441 0.056 0.000 0.000 1.000 0.111
arsenic 2.506 1.282 0.187 2.2 1.10 7.600 0.367
cadmium 0.110 0.041 0.006 0.1 0.03 0.220 0.012
chromium 54.502 25.249 3.683 47.4 10.90 125.000 7.218
copper 6.857 5.021 0.732 5.0 2.20 22.100 1.435
iron 57555.237 30299.094 4419.577 49344.4 14089.92 188857.220 8662.212
manganese 930.994 424.869 61.974 833.6 251.67 2298.700 121.466
mercury 0.007 0.010 0.001 0.0 0.00 0.036 0.003
lead 3.259 1.813 0.265 2.6 1.02 9.500 0.518
zinc 43.540 15.429 2.251 41.7 15.90 101.400 4.411
Cluster 3
  Mean SD SE Median Min Max 95% CI
depth 32.756 19.095 2.024 29.500 2.100 77.000 3.967
om 1.762 0.965 0.102 1.525 0.392 4.415 0.201
gravel 0.017 0.097 0.010 0.000 0.000 0.765 0.020
sand 0.463 0.220 0.023 0.512 0.000 0.868 0.046
silt 0.491 0.209 0.022 0.480 0.021 0.845 0.043
clay 0.030 0.148 0.016 0.000 0.000 0.979 0.031
arsenic 3.886 2.276 0.243 3.050 1.80 16.000 0.475
cadmium 0.137 0.031 0.003 0.140 0.06 0.230 0.007
chromium 58.073 16.012 1.707 58.600 31.10 110.700 3.345
copper 12.334 4.772 0.509 13.100 3.60 28.700 0.997
iron 55265.811 15722.531 1676.027 55752.250 28355.90 151226.400 3284.953
manganese 1180.457 582.751 62.122 1020.300 423.10 3435.200 121.756
mercury 0.024 0.016 0.002 0.021 0.00 0.087 0.003
lead 5.455 1.888 0.201 5.400 1.70 12.100 0.394
zinc 61.091 16.640 1.774 62.950 27.60 141.000 3.477
Diversity
Cluster 1
  Mean SD SE Median Min Max 95% CI
S 27.812 3.781 0.945 28.500 21.000 34.000 1.853
N 1411.188 410.971 102.743 1433.000 636.000 2103.000 201.372
H 1.823 0.268 0.067 1.882 1.246 2.247 0.131
J 0.550 0.081 0.020 0.559 0.409 0.682 0.040
delta 38.494 6.594 1.648 39.262 25.409 49.097 3.231
delta_plus 63.663 1.847 0.462 63.735 58.995 67.300 0.905
delta_star 51.896 4.667 1.167 52.793 44.058 59.625 2.287
Cluster 2
  Mean SD SE Median Min Max 95% CI
S 12.197 6.077 0.778 12.000 1 35.000 1.525
N 110.131 175.663 22.491 56.000 1 941.000 44.082
H 1.619 0.612 0.078 1.623 0 2.737 0.154
J 0.683 0.213 0.027 0.706 0 1.000 0.053
delta 50.139 16.129 2.065 53.777 0 77.778 4.048
delta_plus 68.787 9.821 1.257 70.351 0 77.778 2.465
delta_star 70.279 10.910 1.397 72.837 0 79.812 2.738
Cluster 3
  Mean SD SE Median Min Max 95% CI
S 13.270 4.871 0.516 13.000 4.000 24.000 1.012
N 92.180 71.364 7.565 75.000 4.000 450.000 14.826
H 1.896 0.430 0.046 1.971 0.975 2.577 0.089
J 0.758 0.129 0.014 0.781 0.402 1.000 0.027
delta 55.694 9.436 1.000 58.642 28.289 75.926 1.960
delta_plus 70.368 1.994 0.211 70.370 63.333 75.926 0.414
delta_star 71.045 3.169 0.336 71.358 59.142 79.779 0.658

Here are the graphs plotting specific richness and taxonomic distinctness:

As a measure of \(\beta\) diversity, mean Bray-Curtis dissimilarity is:

  • 0.38 within cluster 1
  • 0.85 within cluster 2
  • 0.69 within cluster 3
Characteristic taxa
##                             cluster indicator_value probability
## capitella_sp                      1          0.9861       0.001
## nephtys_sp                        1          0.9861       0.001
## prionospio_steenstrupi            1          0.9692       0.001
## phyllodoce_groenlandica           1          0.9664       0.001
## cirratulidae_spp                  1          0.9331       0.001
## phoronida                         1          0.9288       0.001
## scoloplos_armiger                 1          0.8889       0.001
## sarsicytheridea_sp                1          0.8706       0.001
## polychaeta                        1          0.8100       0.001
## limecola_balthica                 1          0.7949       0.001
## sertulariidae_spp                 1          0.7531       0.001
## eteone_sp                         1          0.7064       0.001
## bipalponephtys_neotena            1          0.6982       0.001
## campanulariidae_spp               1          0.6946       0.001
## harpacticoida                     1          0.5711       0.001
## euchone_analis                    1          0.5625       0.001
## pholoe_longa                      1          0.5096       0.001
## podocopida                        1          0.4323       0.001
## glycera_dibranchiata              1          0.4297       0.001
## hediste_diversicolor              1          0.4257       0.001
## tharyx_sp                         1          0.3750       0.001
## diastylis_sculpta                 1          0.3651       0.001
## phoxocephalus_holbolli            1          0.3346       0.003
## pholoe_minuta_tecta               1          0.3282       0.001
## praxillella_praetermissa          1          0.3099       0.001
## microphthalmus_sczelkowii         1          0.3009       0.001
## aricidea_sp                       1          0.2998       0.001
## sabellidae_spp                    1          0.2959       0.002
## solenoidea                        1          0.2878       0.001
## microphthalmus_sp                 1          0.2500       0.001
## pontoporeia_femorata              1          0.2477       0.023
## axinopsida_orbiculata             1          0.2457       0.006
## eucratea_loricata                 1          0.2286       0.002
## bivalvia                          1          0.2090       0.004
## cylichna_alba                     1          0.1875       0.002
## harmothoe_imbricata               1          0.1875       0.001
## eudendriidae_spp                  1          0.1724       0.003
## gammaridea                        1          0.1680       0.003
## hemicythere_villosa               1          0.1624       0.002
## spio_filicornis                   1          0.1403       0.013
## eteone_longa                      1          0.1250       0.009
## hartmania_moorei                  1          0.1250       0.008
## macoma_sp                         1          0.1250       0.007
## monticellina_sp                   1          0.1250       0.009
## pherusa_sp                        1          0.1250       0.014
## scoletoma_tetraura                1          0.1250       0.014
## capitellidae_spp                  1          0.1193       0.013
## brachyura                         1          0.1183       0.005
## serripes_groenlandicus            1          0.0509       0.045
## echinarachnius_parma              2          0.5050       0.001
## nematoda                          2          0.3257       0.007
## spisula_solidissima               2          0.2951       0.002
## crenella_decussata                2          0.2317       0.006
## annelida                          2          0.2131       0.005
## polygordius_sp                    2          0.1894       0.019
## nephtys_caeca                     2          0.1754       0.024
## orchomenella_minuta               2          0.1003       0.044
## halacaridae_spp                   2          0.0984       0.042
## ophiura_robusta                   2          0.0984       0.027
## lepeta_caeca                      2          0.0912       0.031
## hiatella_arctica                  2          0.0899       0.050
## macoma_calcarea                   3          0.6715       0.001
## eudorellopsis_integra             3          0.6687       0.001
## ennucula_tenuis                   3          0.4903       0.001
## leucon_leucon_nasicoides          3          0.4655       0.001
## goniada_maculata                  3          0.4399       0.001
## protomedeia_grandimana            3          0.3599       0.003
## ostracoda                         3          0.3403       0.005
## nephtys_incisa                    3          0.3293       0.002
## thyasira_gouldi                   3          0.3186       0.002
## akanthophoreus_gracilis           3          0.3118       0.003
## amphipoda                         3          0.2632       0.027
## quasimelita_formosa               3          0.2374       0.009
## aceroides_aceroides_latipes       3          0.2267       0.010
## chaetodermatida                   3          0.1566       0.025
## sipuncula                         3          0.1410       0.047
## 
## Sum of probabilities                 =  81.499 
## 
## Sum of Indicator Values              =  35.53 
## 
## Sum of Significant Indicator Values  =  28.89 
## 
## Number of Significant Indicators     =  76 
## 
## Significant Indicator Distribution
## 
##  1  2  3 
## 49 12 15
Phylum abundances by cluster
phylum cl1 cl2 cl3
Annelida 20206 1497 3099
Phoronida 1137 3 0
Arthropoda 861 2199 3472
Mollusca 327 1404 1168
Cnidaria 36 50 0
Bryozoa 6 24 0
Echinodermata 4 538 37
Hemichordata 2 0 0
Chaetognatha 0 1 0
Nematoda 0 982 395
Nemertea 0 17 4
Sipuncula 0 3 29

SIMPER results between clusters 1 and 2 (mean between-group Bray-Curtis dissimilarity: 0.925)
  average sd ratio ava avb cumsum
bipalponephtys_neotena 0.0735 0.0147 4.99 6.17 0.278 0.0794
nephtys_sp 0.0726 0.0124 5.85 5.92 0.0682 0.158
prionospio_steenstrupi 0.0494 0.0129 3.84 4.09 0.13 0.211
phoronida 0.0439 0.0162 2.71 3.64 0.0341 0.259
scoloplos_armiger 0.0437 0.0189 2.32 3.7 0.202 0.306
capitella_sp 0.0392 0.0111 3.53 3.23 0.0455 0.348
phyllodoce_groenlandica 0.0382 0.0101 3.8 3.23 0.113 0.39
cirratulidae_spp 0.0298 0.0122 2.45 2.42 0.0114 0.422
sarsicytheridea_sp 0.027 0.0139 1.95 2.26 0.0114 0.451
limecola_balthica 0.0251 0.0154 1.63 2.05 0.0455 0.478
harpacticoida 0.0211 0.0129 1.63 2.21 0.902 0.501
eteone_sp 0.0192 0.0129 1.49 1.62 0.0768 0.522
phoxocephalus_holbolli 0.0169 0.0133 1.27 1.36 1.02 0.54
euchone_analis 0.0164 0.017 0.964 1.43 0 0.558
nematoda 0.0151 0.02 0.756 0 1.21 0.574
pholoe_sp 0.0149 0.0145 1.03 1.19 0.258 0.59
pholoe_longa 0.0145 0.0141 1.03 1.21 0.0965 0.606
echinarachnius_parma 0.0134 0.0139 0.966 0 1.1 0.62
pholoe_minuta_tecta 0.0123 0.0162 0.757 0.959 0.137 0.633
podocopida 0.0112 0.0154 0.727 0.944 0.0114 0.646
sabellidae_spp 0.0108 0.0152 0.708 0.909 0.0114 0.657
pontoporeia_femorata 0.0104 0.013 0.8 0.804 0.0114 0.668
hediste_diversicolor 0.0102 0.00999 1.02 0.842 0.102 0.679
microphthalmus_sczelkowii 0.0094 0.0143 0.657 0.761 0.0294 0.69
diastylis_sculpta 0.00927 0.0122 0.758 0.783 0.0114 0.7
SIMPER results between clusters 1 and 3 (mean between-group Bray-Curtis dissimilarity: 0.904)
  average sd ratio ava avb cumsum
nephtys_sp 0.07 0.0112 6.23 5.92 0.0156 0.0774
prionospio_steenstrupi 0.0487 0.0107 4.56 4.09 0 0.131
bipalponephtys_neotena 0.0457 0.0192 2.38 6.17 2.39 0.182
scoloplos_armiger 0.0438 0.0176 2.49 3.7 0 0.23
phoronida 0.0423 0.0155 2.73 3.64 0 0.277
capitella_sp 0.0379 0.0102 3.73 3.23 0 0.319
phyllodoce_groenlandica 0.0379 0.0086 4.4 3.23 0 0.361
cirratulidae_spp 0.0286 0.0116 2.47 2.42 0 0.393
sarsicytheridea_sp 0.026 0.0133 1.95 2.26 0 0.421
limecola_balthica 0.0244 0.0149 1.64 2.05 0 0.448
harpacticoida 0.0215 0.012 1.78 2.21 0.519 0.472
eudorellopsis_integra 0.0191 0.0161 1.18 0.0687 1.68 0.493
eteone_sp 0.0187 0.0124 1.5 1.62 0.0234 0.514
macoma_calcarea 0.0158 0.0112 1.41 0.0687 1.4 0.531
euchone_analis 0.0157 0.0163 0.964 1.43 0 0.549
phoxocephalus_holbolli 0.0153 0.0128 1.19 1.36 0.156 0.566
pholoe_sp 0.0143 0.0118 1.21 1.19 0.702 0.582
pholoe_longa 0.014 0.0138 1.01 1.21 0.0297 0.597
pontoporeia_femorata 0.0124 0.0129 0.965 0.804 0.605 0.611
sabellidae_spp 0.0114 0.0149 0.768 0.909 0.232 0.623
pholoe_minuta_tecta 0.0112 0.0156 0.716 0.959 0 0.636
podocopida 0.0107 0.0147 0.724 0.944 0 0.648
hediste_diversicolor 0.00966 0.00911 1.06 0.842 0.169 0.658
leucon_leucon_nasicoides 0.00947 0.0124 0.765 0 0.828 0.669
diastylis_sculpta 0.00943 0.0117 0.806 0.783 0.144 0.679
protomedeia_grandimana 0.0092 0.011 0.836 0 0.803 0.689
axinopsida_orbiculata 0.00915 0.011 0.831 0.677 0.322 0.7
SIMPER results between clusters 2 and 3 (mean between-group Bray-Curtis dissimilarity: 0.917)
  average sd ratio ava avb cumsum
bipalponephtys_neotena 0.065 0.0407 1.6 0.278 2.39 0.0709
eudorellopsis_integra 0.0484 0.0439 1.1 0.0294 1.68 0.124
nematoda 0.0427 0.0502 0.85 1.21 0.676 0.17
macoma_calcarea 0.0396 0.0335 1.18 0.289 1.4 0.213
echinarachnius_parma 0.0331 0.0368 0.901 1.1 0.15 0.25
phoxocephalus_holbolli 0.0289 0.034 0.85 1.02 0.156 0.281
harpacticoida 0.0277 0.0291 0.952 0.902 0.519 0.311
protomedeia_grandimana 0.0265 0.0344 0.768 0.225 0.803 0.34
leucon_leucon_nasicoides 0.0232 0.0308 0.753 0.0114 0.828 0.365
ennucula_tenuis 0.0216 0.0246 0.877 0.0774 0.774 0.389
pholoe_sp 0.0211 0.0228 0.926 0.258 0.702 0.412
spisula_solidissima 0.0209 0.0386 0.542 0.753 0 0.435
pontoporeia_femorata 0.0186 0.0339 0.549 0.0114 0.605 0.455
amphipoda 0.0156 0.021 0.741 0.185 0.452 0.472
goniada_maculata 0.0149 0.0202 0.736 0.0114 0.523 0.488
maldanidae_spp 0.0148 0.0282 0.523 0.0114 0.529 0.504
ostracoda 0.0138 0.0208 0.664 0.0455 0.507 0.52
thyasira_gouldi 0.0134 0.0224 0.599 0.0114 0.498 0.534
akanthophoreus_gracilis 0.0131 0.0212 0.619 0.0227 0.506 0.548
nephtys_incisa 0.0109 0.0164 0.668 0.0341 0.371 0.56
oligochaeta 0.0107 0.0258 0.415 0.166 0.266 0.572
polynoidae_spp 0.0106 0.0181 0.584 0.0638 0.341 0.584
axinopsida_orbiculata 0.0103 0.0222 0.462 0.0341 0.322 0.595
crenella_decussata 0.00963 0.02 0.482 0.328 0.0201 0.605
mytilus_sp 0.00954 0.0219 0.436 0.341 0.0824 0.616
thracia_septentrionalis 0.0094 0.0201 0.467 0.19 0.183 0.626
caprella_septentrionalis 0.00934 0.0273 0.342 0.33 0.0549 0.636
quasimelita_formosa 0.00927 0.0175 0.53 0.0294 0.332 0.646
polygordius_sp 0.00916 0.0207 0.443 0.359 0 0.656
cistenides_granulata 0.00881 0.0162 0.543 0.217 0.145 0.666
nephtys_caeca 0.00802 0.0146 0.55 0.226 0.0435 0.675
aceroides_aceroides_latipes 0.00741 0.0149 0.497 0.0114 0.279 0.683
hediste_diversicolor 0.0073 0.0182 0.401 0.102 0.169 0.691
ameritella_agilis 0.00717 0.0166 0.432 0.192 0.0591 0.698
1 mm community
Habitat and metals
Cluster 1
  Mean SD SE Median Min Max 95% CI
depth 21.501 17.095 1.876 18.700 1.0 66.600 3.678
om 0.718 0.699 0.077 0.476 0.2 3.872 0.150
gravel 0.068 0.152 0.017 0.000 0.0 0.809 0.033
sand 0.638 0.335 0.037 0.746 0.0 1.000 0.072
silt 0.250 0.273 0.030 0.101 0.0 0.942 0.059
clay 0.044 0.108 0.012 0.000 0.0 0.497 0.023
arsenic 3.564 3.575 0.552 2.600 1.10 21.30 1.081
cadmium 0.132 0.036 0.006 0.130 0.07 0.22 0.011
chromium 51.767 22.888 3.532 45.500 17.00 111.00 6.922
copper 9.300 6.826 1.053 8.100 2.40 28.80 2.064
iron 49195.114 15793.514 2436.992 45131.850 21938.10 86123.70 4776.417
manganese 857.457 378.804 58.451 761.900 318.40 2098.00 114.561
mercury 0.017 0.011 0.002 0.014 0.00 0.04 0.003
lead 4.645 2.614 0.403 3.850 2.00 12.10 0.790
zinc 54.281 26.473 4.085 46.700 26.90 141.40 8.006
Cluster 2
  Mean SD SE Median Min Max 95% CI
depth 16.111 13.159 2.533 13.300 2.600 54.200 4.964
om 0.353 0.179 0.034 0.306 0.168 0.895 0.067
gravel 0.022 0.047 0.009 0.000 0.000 0.152 0.018
sand 0.924 0.093 0.018 0.969 0.614 1.001 0.035
silt 0.047 0.063 0.012 0.021 0.000 0.227 0.024
clay 0.006 0.012 0.002 0.002 0.000 0.061 0.005
arsenic 1.833 0.896 0.517 2.30 0.800 2.400 1.014
cadmium 0.097 0.006 0.003 0.10 0.090 0.100 0.007
chromium 32.733 9.122 5.267 32.00 24.000 42.200 10.322
copper 6.367 2.401 1.386 5.40 4.600 9.100 2.717
iron 33559.100 7909.177 4566.366 29711.60 28309.800 42655.900 8949.912
manganese 569.767 148.562 85.772 567.60 422.300 719.400 168.110
mercury 0.011 0.005 0.003 0.01 0.007 0.017 0.006
lead 2.633 0.635 0.367 3.00 1.900 3.000 0.719
zinc 39.767 5.391 3.113 39.00 34.800 45.500 6.100
Cluster 3
  Mean SD SE Median Min Max 95% CI
depth 32.519 19.270 2.043 29.700 1.900 77.000 4.003
om 1.769 0.967 0.102 1.529 0.288 4.415 0.201
gravel 0.017 0.098 0.010 0.000 0.000 0.765 0.020
sand 0.470 0.235 0.025 0.498 0.000 0.979 0.049
silt 0.484 0.234 0.025 0.480 0.017 0.881 0.049
clay 0.029 0.143 0.015 0.000 0.000 0.979 0.030
arsenic 3.958 2.382 0.253 3.100 1.70 16.000 0.495
cadmium 0.142 0.035 0.004 0.140 0.06 0.270 0.007
chromium 58.193 15.648 1.659 58.000 31.10 106.000 3.251
copper 12.255 4.735 0.502 12.900 3.00 26.300 0.984
iron 54963.176 15905.789 1686.010 55153.500 28355.90 151226.400 3304.519
manganese 1174.708 587.250 62.248 1019.800 423.10 3435.200 122.005
mercury 0.026 0.017 0.002 0.022 0.00 0.091 0.004
lead 5.590 1.860 0.197 5.500 1.70 12.200 0.386
zinc 61.607 15.872 1.682 63.600 27.60 130.000 3.297
Diversity
Cluster 1
  Mean SD SE Median Min Max 95% CI
S 6.590 3.787 0.416 6.000 1 17.000 0.815
N 39.590 84.647 9.291 17.000 1 674.000 18.211
H 1.313 0.552 0.061 1.332 0 2.555 0.119
J 0.767 0.213 0.023 0.821 0 1.000 0.046
delta 55.169 18.598 2.041 58.974 0 87.500 4.001
delta_plus 77.601 13.916 1.528 81.071 0 87.500 2.994
delta_star 77.267 14.604 1.603 81.250 0 87.500 3.142
Cluster 2
  Mean SD SE Median Min Max 95% CI
S 3.037 1.675 0.322 3.000 1 8.000 0.632
N 18.148 22.127 4.258 9.000 2 96.000 8.346
H 0.688 0.451 0.087 0.639 0 1.475 0.170
J 0.606 0.338 0.065 0.715 0 1.000 0.127
delta 39.144 26.038 5.011 43.750 0 87.500 9.822
delta_plus 71.099 30.464 5.863 82.589 0 87.500 11.491
delta_star 72.177 30.873 5.942 86.061 0 87.500 11.645
Cluster 3
  Mean SD SE Median Min Max 95% CI
S 10.112 3.918 0.415 10.000 3.000 19.000 0.814
N 47.483 38.162 4.045 39.000 3.000 254.000 7.928
H 1.784 0.462 0.049 1.899 0.643 2.646 0.096
J 0.803 0.134 0.014 0.838 0.433 1.000 0.028
delta 62.022 12.187 1.292 65.556 20.356 79.167 2.532
delta_plus 79.668 2.591 0.275 79.722 71.250 85.000 0.538
delta_star 78.875 4.589 0.486 79.453 63.580 88.001 0.953

Here are the graphs plotting specific richness and taxonomic distinctness:

As a measure of \(\beta\) diversity, mean Bray-Curtis dissimilarity is:

  • 0.93 within cluster 1
  • 0.69 within cluster 2
  • 0.75 within cluster 3
Characteristic taxa
##                             cluster indicator_value probability
## cistenides_granulata              1          0.2935       0.001
## crenella_decussata                1          0.1868       0.002
## phoxocephalus_holbolli            1          0.1651       0.015
## strongylocentrotus_sp             1          0.1267       0.030
## limecola_balthica                 1          0.1264       0.011
## hiatella_arctica                  1          0.1084       0.011
## pagurus_pubescens                 1          0.0843       0.021
## parvicardium_pinnulatum           1          0.0768       0.027
## boreochiton_ruber                 1          0.0723       0.032
## caprella_septentrionalis          1          0.0723       0.034
## lepeta_caeca                      1          0.0723       0.037
## ophiura_robusta                   1          0.0723       0.028
## ampharetidae_spp                  1          0.0602       0.025
## echinarachnius_parma              2          0.5561       0.001
## mesodesma_arctatum                2          0.4543       0.001
## nephtys_bucera                    2          0.0913       0.018
## eudorellopsis_integra             3          0.6416       0.001
## macoma_calcarea                   3          0.6097       0.001
## ennucula_tenuis                   3          0.5109       0.001
## bipalponephtys_neotena            3          0.5016       0.001
## goniada_maculata                  3          0.4393       0.001
## leucon_leucon_nasicoides          3          0.3708       0.001
## protomedeia_grandimana            3          0.3549       0.001
## pholoe_sp                         3          0.3061       0.001
## pontoporeia_femorata              3          0.2665       0.003
## quasimelita_formosa               3          0.2593       0.001
## nephtys_incisa                    3          0.2587       0.002
## thyasira_gouldi                   3          0.2575       0.001
## maldanidae_spp                    3          0.2411       0.001
## polynoidae_spp                    3          0.2070       0.006
## oligochaeta                       3          0.1605       0.006
## axinopsida_orbiculata             3          0.1492       0.006
## aceroides_aceroides_latipes       3          0.1385       0.002
## chaetodermatida                   3          0.1255       0.018
## hediste_diversicolor              3          0.1243       0.024
## protomedeia_fasciata              3          0.1236       0.011
## sipuncula                         3          0.1109       0.021
## nuculana_minuta                   3          0.1025       0.027
## akanthophoreus_gracilis           3          0.1011       0.020
## praxillella_praetermissa          3          0.0894       0.037
## diastylis_rathkei                 3          0.0787       0.034
## eudorella_emarginata              3          0.0674       0.046
## 
## Sum of probabilities                 =  68.843 
## 
## Sum of Indicator Values              =  12.8 
## 
## Sum of Significant Indicator Values  =  9.22 
## 
## Number of Significant Indicators     =  42 
## 
## Significant Indicator Distribution
## 
##  1  2  3 
## 13  3 26
Phylum abundances by cluster
phylum cl1 cl2 cl3
Arthropoda 1294 28 1743
Mollusca 661 199 905
Annelida 598 27 1499
Echinodermata 562 235 31
Nematoda 145 1 19
Nemertea 13 0 4
Cnidaria 6 0 0
Sipuncula 4 0 23
Brachiopoda 2 0 0
Platyhelminthes 1 0 2

SIMPER results between clusters 1 and 2 (mean between-group Bray-Curtis dissimilarity: 0.922)
  average sd ratio ava avb cumsum
echinarachnius_parma 0.137 0.128 1.07 0.513 1.54 0.149
mesodesma_arctatum 0.0969 0.124 0.779 0.0664 1.11 0.254
cistenides_granulata 0.0482 0.0816 0.591 0.533 0.0257 0.306
strongylocentrotus_sp 0.0311 0.0626 0.497 0.27 0.133 0.34
phoxocephalus_holbolli 0.0296 0.0597 0.495 0.341 0.0721 0.372
nephtys_caeca 0.027 0.0439 0.615 0.184 0.169 0.401
nematoda 0.0202 0.0602 0.336 0.255 0.0257 0.423
macoma_calcarea 0.0201 0.046 0.436 0.257 0.0721 0.445
limecola_balthica 0.0185 0.0479 0.387 0.178 0.0257 0.465
protomedeia_grandimana 0.0163 0.0526 0.309 0.245 0 0.483
crenella_decussata 0.0163 0.039 0.418 0.244 0 0.501
amphipholis_squamata 0.0157 0.051 0.308 0.125 0.0513 0.518
scoloplos_armiger 0.0154 0.0505 0.306 0.143 0.0407 0.534
ameritella_agilis 0.0145 0.0463 0.313 0.161 0.0257 0.55
harmothoe_imbricata 0.0133 0.042 0.318 0.0815 0.0407 0.564
orchomenella_minuta 0.0127 0.0394 0.323 0.0626 0.092 0.578
mya_arenaria 0.0123 0.0307 0.402 0.0951 0.0513 0.592
caprella_septentrionalis 0.0104 0.0398 0.261 0.217 0 0.603
ciliatocardium_ciliatum 0.0102 0.0394 0.259 0.0564 0.0596 0.614
psammonyx_nobilis 0.00952 0.0412 0.231 0.0652 0.0257 0.624
diastylis_sculpta 0.00939 0.0362 0.259 0.0873 0.0257 0.635
hiatella_arctica 0.00873 0.0311 0.281 0.13 0 0.644
ophelia_limacina 0.00866 0.0265 0.327 0.0766 0.0257 0.653
ophiura_robusta 0.00853 0.0347 0.246 0.19 0 0.663
nephtys_bucera 0.0085 0.0241 0.352 0.0167 0.077 0.672
polynoidae_spp 0.00813 0.0251 0.324 0.109 0.0257 0.681
pholoe_sp 0.00738 0.0236 0.313 0.0528 0.0513 0.689
nephtys_incisa 0.00738 0.0232 0.318 0.0633 0.0257 0.697
SIMPER results between clusters 1 and 3 (mean between-group Bray-Curtis dissimilarity: 0.948)
  average sd ratio ava avb cumsum
eudorellopsis_integra 0.0613 0.0605 1.01 0.0432 1.3 0.0647
macoma_calcarea 0.0613 0.0595 1.03 0.257 1.13 0.129
protomedeia_grandimana 0.0415 0.0494 0.841 0.245 0.823 0.173
bipalponephtys_neotena 0.0389 0.0507 0.767 0.0444 0.842 0.214
ennucula_tenuis 0.0378 0.0424 0.891 0.0766 0.769 0.254
cistenides_granulata 0.0283 0.0439 0.645 0.533 0.119 0.284
pontoporeia_femorata 0.028 0.0494 0.566 0.0768 0.555 0.313
goniada_maculata 0.0268 0.0326 0.823 0.0432 0.549 0.342
echinarachnius_parma 0.0265 0.0416 0.637 0.513 0.102 0.37
maldanidae_spp 0.024 0.0451 0.533 0.0611 0.516 0.395
leucon_leucon_nasicoides 0.0238 0.0387 0.616 0 0.527 0.42
pholoe_sp 0.0207 0.032 0.649 0.0528 0.42 0.442
thyasira_gouldi 0.0199 0.0345 0.578 0.0383 0.421 0.463
phoxocephalus_holbolli 0.0179 0.0343 0.522 0.341 0.0849 0.482
polynoidae_spp 0.0175 0.0283 0.618 0.109 0.328 0.5
nephtys_incisa 0.0172 0.026 0.659 0.0633 0.343 0.518
quasimelita_formosa 0.0167 0.0308 0.542 0.0299 0.36 0.536
nephtys_caeca 0.0152 0.0302 0.504 0.184 0.124 0.552
nematoda 0.0142 0.0379 0.374 0.255 0.0915 0.567
axinopsida_orbiculata 0.0138 0.0334 0.412 0.0334 0.257 0.582
strongylocentrotus_sp 0.013 0.0336 0.387 0.27 0.00779 0.595
oligochaeta 0.0112 0.0294 0.382 0.0132 0.266 0.607
thracia_septentrionalis 0.0111 0.0305 0.362 0.13 0.128 0.619
crenella_decussata 0.0105 0.025 0.419 0.244 0.00779 0.63
ameritella_agilis 0.00922 0.0275 0.336 0.161 0.0357 0.64
limecola_balthica 0.00891 0.025 0.356 0.178 0 0.649
thyasira_sp 0.00847 0.0307 0.276 0.0277 0.126 0.658
diastylis_sculpta 0.00835 0.0263 0.318 0.0873 0.0883 0.667
scoloplos_armiger 0.00828 0.0293 0.282 0.143 0.0304 0.675
hediste_diversicolor 0.00814 0.0199 0.409 0.0299 0.171 0.684
praxillella_praetermissa 0.0079 0.0308 0.256 0.0167 0.128 0.692
SIMPER results between clusters 2 and 3 (mean between-group Bray-Curtis dissimilarity: 0.979)
  average sd ratio ava avb cumsum
echinarachnius_parma 0.0938 0.079 1.19 1.54 0.102 0.0958
macoma_calcarea 0.0756 0.0711 1.06 0.0721 1.13 0.173
eudorellopsis_integra 0.073 0.0701 1.04 0 1.3 0.248
mesodesma_arctatum 0.064 0.0819 0.781 1.11 0 0.313
bipalponephtys_neotena 0.0453 0.0586 0.773 0 0.842 0.359
ennucula_tenuis 0.0451 0.0499 0.903 0 0.769 0.405
protomedeia_grandimana 0.0429 0.0521 0.823 0 0.823 0.449
goniada_maculata 0.0308 0.0367 0.84 0.0257 0.549 0.481
pontoporeia_femorata 0.0301 0.055 0.547 0 0.555 0.511
leucon_leucon_nasicoides 0.028 0.0445 0.629 0 0.527 0.54
maldanidae_spp 0.0261 0.0526 0.497 0 0.516 0.567
pholoe_sp 0.0241 0.0363 0.665 0.0513 0.42 0.591
thyasira_gouldi 0.0221 0.0396 0.56 0 0.421 0.614
nephtys_incisa 0.0191 0.0297 0.644 0.0257 0.343 0.634
quasimelita_formosa 0.0187 0.0354 0.529 0 0.36 0.653
polynoidae_spp 0.0184 0.0311 0.592 0.0257 0.328 0.672
nephtys_caeca 0.0169 0.0333 0.508 0.169 0.124 0.689

2. Regressions

2.1. Data manipulation

For the following analyses, independant variables are habitat parameters and heavy metal concentrations, dependant variables are diversity indices. Variables have been standardized by mean and standard-deviation.

2.1.1. Identification of outliers

To identify stations that are not consistent with the others, we used the multivariate Cook’s Distance (CD) on the uncorrelated variables. A significative threshold of 4 times the mean of CD has been established.

0.5 mm community

We identified the following stations as general outliers:

  • stations 30, 127, 138, 144, 183, 228, 237 for habitat
  • stations 1, 11, 22, 25, 35, 127, 132, 139, 231 for metals

They have been deleted for the following analyses.

1 mm community

We identified the following stations as general outliers:

  • stations 72, 82, 107, 129, 144, 202, 249 for habitat
  • stations 106, 108, 110, 120, 127, 130, 139, 154, 232 for metals

They have been deleted for the following analyses.

2.1.2. Correlations between predictors

Correlations have been calculated with Spearman’s rank coefficient.

0.5 mm community

According to these results, the following variables are highly correlated (\(|\rho|\) > 0.80) so they have been considered together in the regressions:

  • chromium, iron and manganese (iron and manganese deleted)
  • copper, lead and zinc (copper and lead deleted)

We decided to exclude silt content, as it tends to drastically increase VIFs due to a marginal correlation with organic matter (\(R^{2}_{adj}\) = 0.21).

Correlation coefficients between habitat parameters (0.5 mm community subset)
  depth om gravel sand silt clay delta delta_plus delta_star
depth 1 0.298 -0.26 0.209 0.497 -0.483 0.468 0.361 0.211
om 0.298 1 -0.504 -0.407 0.479 0.01 -0.074 -0.237 -0.368
gravel -0.26 -0.504 1 0.019 -0.347 0.162 0.076 0.001 0.147
sand 0.209 -0.407 0.019 1 -0.007 -0.749 0.128 0.35 0.348
silt 0.497 0.479 -0.347 -0.007 1 -0.534 0.214 0.284 0.087
clay -0.483 0.01 0.162 -0.749 -0.534 1 -0.263 -0.432 -0.363
delta 0.468 -0.074 0.076 0.128 0.214 -0.263 1 0.252 0.27
delta_plus 0.361 -0.237 0.001 0.35 0.284 -0.432 0.252 1 0.618
delta_star 0.211 -0.368 0.147 0.348 0.087 -0.363 0.27 0.618 1
Correlation coefficients between metals (0.5 mm community subset)
  arsenic cadmium chromium copper iron manganese mercury lead zinc delta delta_plus delta_star
arsenic 1 0.732 0.631 0.713 0.405 0.62 0.711 0.865 0.808 -0.137 -0.147 -0.329
cadmium 0.732 1 0.784 0.691 0.514 0.669 0.66 0.854 0.838 -0.245 -0.084 -0.322
chromium 0.631 0.784 1 0.738 0.8 0.891 0.467 0.75 0.792 -0.333 -0.27 -0.361
copper 0.713 0.691 0.738 1 0.571 0.738 0.612 0.843 0.928 -0.283 -0.286 -0.529
iron 0.405 0.514 0.8 0.571 1 0.83 0.187 0.458 0.571 -0.269 -0.142 -0.266
manganese 0.62 0.669 0.891 0.738 0.83 1 0.463 0.681 0.738 -0.321 -0.302 -0.434
mercury 0.711 0.66 0.467 0.612 0.187 0.463 1 0.787 0.683 -0.085 0.027 -0.25
lead 0.865 0.854 0.75 0.843 0.458 0.681 0.787 1 0.928 -0.251 -0.181 -0.426
zinc 0.808 0.838 0.792 0.928 0.571 0.738 0.683 0.928 1 -0.271 -0.195 -0.458
delta -0.137 -0.245 -0.333 -0.283 -0.269 -0.321 -0.085 -0.251 -0.271 1 0.312 0.352
delta_plus -0.147 -0.084 -0.27 -0.286 -0.142 -0.302 0.027 -0.181 -0.195 0.312 1 0.575
delta_star -0.329 -0.322 -0.361 -0.529 -0.266 -0.434 -0.25 -0.426 -0.458 0.352 0.575 1

1 mm community

According to these results, the following variables are highly correlated (\(|\rho|\) > 0.80) so they have been considered together in the regressions:

  • om and silt (silt deleted)
  • chromium, iron and manganese (iron and manganese deleted)
  • copper, lead and zinc (copper and lead deleted)
Correlation coefficients between habitat parameters (1 mm community subset)
  depth om gravel sand silt clay delta delta_plus delta_star
depth 1 0.442 -0.026 -0.328 0.317 -0.097 0.497 0.113 0.138
om 0.442 1 -0.304 -0.798 0.841 -0.125 0.273 -0.116 -0.163
gravel -0.026 -0.304 1 0.124 -0.383 -0.023 -0.099 0.027 -0.001
sand -0.328 -0.798 0.124 1 -0.927 -0.12 -0.19 0.143 0.169
silt 0.317 0.841 -0.383 -0.927 1 0.069 0.216 -0.133 -0.155
clay -0.097 -0.125 -0.023 -0.12 0.069 1 -0.112 -0.102 -0.113
delta 0.497 0.273 -0.099 -0.19 0.216 -0.112 1 0.166 0.17
delta_plus 0.113 -0.116 0.027 0.143 -0.133 -0.102 0.166 1 0.788
delta_star 0.138 -0.163 -0.001 0.169 -0.155 -0.113 0.17 0.788 1
Correlation coefficients between metals (1 mm community subset)
  arsenic cadmium chromium copper iron manganese mercury lead zinc delta delta_plus delta_star
arsenic 1 0.743 0.79 0.809 0.636 0.707 0.702 0.892 0.88 -0.053 0.016 -0.123
cadmium 0.743 1 0.772 0.639 0.541 0.662 0.669 0.833 0.812 -0.257 -0.109 -0.291
chromium 0.79 0.772 1 0.858 0.823 0.902 0.667 0.837 0.892 -0.156 -0.009 -0.224
copper 0.809 0.639 0.858 1 0.77 0.792 0.708 0.857 0.946 -0.092 0.014 -0.164
iron 0.636 0.541 0.823 0.77 1 0.869 0.412 0.595 0.753 -0.099 0.097 -0.086
manganese 0.707 0.662 0.902 0.792 0.869 1 0.573 0.705 0.79 -0.148 0.023 -0.199
mercury 0.702 0.669 0.667 0.708 0.412 0.573 1 0.844 0.743 -0.138 -0.12 -0.275
lead 0.892 0.833 0.837 0.857 0.595 0.705 0.844 1 0.928 -0.128 -0.102 -0.275
zinc 0.88 0.812 0.892 0.946 0.753 0.79 0.743 0.928 1 -0.137 -0.004 -0.194
delta -0.053 -0.257 -0.156 -0.092 -0.099 -0.148 -0.138 -0.128 -0.137 1 0.231 0.318
delta_plus 0.016 -0.109 -0.009 0.014 0.097 0.023 -0.12 -0.102 -0.004 0.231 1 0.703
delta_star -0.123 -0.291 -0.224 -0.164 -0.086 -0.199 -0.275 -0.275 -0.194 0.318 0.703 1

2.2. Univariate regressions

We used linear models for the regressions on diversity indices. Outliers and correlated variables were removed from these analyses. Variables have been standardized by mean and standard-deviation (coefficients need to be back-transformed to be used in predictive models).

We identified which variables were selected after an AIC procedure to predict the best the parameters. Results of the variable selection, according to AIC, and details of the regressions, with diagnostics and cross-validation, are summarized below.

0.5 mm community
Variable (or combination) S N H J
depth + - + +
om/silt + + -
gravel + + +
sand + + -
clay + + + -
Adjusted \(R^{2}\) 0.33 0.5 0.27 0.16
Variable (or combination) S N H J
arsenic - -
cadmium - -
chromium/iron/manganese + - -
mercury - -
lead/copper/zinc + +
Adjusted \(R^{2}\) 0.18 0.49 0.07 0.02
Richness/habitat
## FULL MODEL
## Adjusted R2 is: 0.33
Fitting linear model: S ~ depth + om + gravel + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.02378 0.06373 0.3732 0.7095
depth 0.2425 0.07287 3.328 0.001097 * *
om 0.2862 0.07987 3.584 0.0004548 * * *
gravel 0.2016 0.09011 2.237 0.0267 *
sand 0.2542 0.1164 2.183 0.03054 *
clay 0.7458 0.1126 6.623 5.636e-10 * * * 
## RMSE from cross-validation: 0.8090011
Variance Inflation Factors
  depth om gravel sand clay
VIF 1.14 1.25 1.05 1.84 1.79 

## REDUCED MODEL
## Adjusted R2 is: 0.33
Fitting linear model: S ~ depth + om + gravel + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.02378 0.06373 0.3732 0.7095
depth 0.2425 0.07287 3.328 0.001097 * *
om 0.2862 0.07987 3.584 0.0004548 * * *
gravel 0.2016 0.09011 2.237 0.0267 *
sand 0.2542 0.1164 2.183 0.03054 *
clay 0.7458 0.1126 6.623 5.636e-10 * * * 
## RMSE from cross-validation: 0.8090011
Variance Inflation Factors
  depth om gravel sand clay
VIF 1.14 1.25 1.05 1.84 1.79

Density/habitat
## FULL MODEL
## Adjusted R2 is: 0.5
Fitting linear model: N ~ depth + om + gravel + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.01472 0.05775 0.2548 0.7992
depth -0.09499 0.06604 -1.438 0.1524
om 0.5369 0.07238 7.418 7.649e-12 * * *
gravel 0.1221 0.08167 1.495 0.137
sand 0.5185 0.1055 4.914 2.27e-06 * * *
clay 0.8915 0.1021 8.736 3.96e-15 * * * 
## RMSE from cross-validation: 0.7850903
Variance Inflation Factors
  depth om gravel sand clay
VIF 1.14 1.25 1.05 1.84 1.79 

## REDUCED MODEL
## Adjusted R2 is: 0.5
Fitting linear model: N ~ depth + om + gravel + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.01472 0.05775 0.2548 0.7992
depth -0.09499 0.06604 -1.438 0.1524
om 0.5369 0.07238 7.418 7.649e-12 * * *
gravel 0.1221 0.08167 1.495 0.137
sand 0.5185 0.1055 4.914 2.27e-06 * * *
clay 0.8915 0.1021 8.736 3.96e-15 * * * 
## RMSE from cross-validation: 0.7850903
Variance Inflation Factors
  depth om gravel sand clay
VIF 1.14 1.25 1.05 1.84 1.79

Diversity/habitat
## FULL MODEL
## Adjusted R2 is: 0.26
Fitting linear model: H ~ depth + om + gravel + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.05663 0.06356 0.891 0.3743
depth 0.5219 0.07268 7.181 2.822e-11 * * *
om 0.01009 0.07966 0.1267 0.8993
gravel 0.156 0.08988 1.735 0.08467
sand -0.07361 0.1161 -0.6339 0.5271
clay 0.2279 0.1123 2.029 0.04422 * 
## RMSE from cross-validation: 0.8101494
Variance Inflation Factors
  depth om gravel sand clay
VIF 1.14 1.25 1.05 1.84 1.79 

## REDUCED MODEL
## Adjusted R2 is: 0.27
Fitting linear model: H ~ depth + gravel + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.05587 0.06327 0.883 0.3786
depth 0.535 0.07073 7.565 3.221e-12 * * *
gravel 0.1584 0.08587 1.844 0.06707
clay 0.2884 0.06988 4.127 5.989e-05 * * * 
## RMSE from cross-validation: 0.8019987
Variance Inflation Factors
  depth gravel clay
VIF 1.11 1 1.12

Evenness/habitat
## FULL MODEL
## Adjusted R2 is: 0.15
Fitting linear model: J ~ depth + om + gravel + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.05235 0.06563 0.7977 0.4263
depth 0.3094 0.07505 4.122 6.142e-05 * * *
om -0.1443 0.08226 -1.754 0.08146
gravel -0.004248 0.09281 -0.04577 0.9636
sand -0.2053 0.1199 -1.712 0.08889
clay -0.2228 0.116 -1.921 0.05654 
## RMSE from cross-validation: 0.8333639
Variance Inflation Factors
  depth om gravel sand clay
VIF 1.14 1.25 1.05 1.84 1.79 

## REDUCED MODEL
## Adjusted R2 is: 0.16
Fitting linear model: J ~ depth + om + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.05272 0.06493 0.8119 0.4181
depth 0.3095 0.07478 4.138 5.741e-05 * * *
om -0.1432 0.07888 -1.816 0.07132
sand -0.2041 0.1168 -1.748 0.08245
clay -0.2218 0.1134 -1.956 0.05228 
## RMSE from cross-validation: 0.8267531
Variance Inflation Factors
  depth om sand clay
VIF 1.14 1.21 1.8 1.75

Richness/metals
## FULL MODEL
## Adjusted R2 is: 0.17
Fitting linear model: S ~ arsenic + cadmium + chromium + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.06047 0.0758 -0.7978 0.4264
arsenic -0.4048 0.1342 -3.017 0.003051 * *
cadmium -0.6834 0.1672 -4.087 7.426e-05 * * *
chromium -0.01251 0.138 -0.09062 0.9279
mercury -0.4632 0.1701 -2.723 0.007325 * *
lead 1.038 0.2052 5.06 1.333e-06 * * * 
## RMSE from cross-validation: 0.8958299
Variance Inflation Factors
  arsenic cadmium chromium mercury lead
VIF 1.56 2.08 1.75 1.43 2.7 

## REDUCED MODEL
## Adjusted R2 is: 0.18
Fitting linear model: S ~ arsenic + cadmium + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.05982 0.07518 -0.7956 0.4276
arsenic -0.4045 0.1337 -3.026 0.002957 * *
cadmium -0.6892 0.154 -4.474 1.6e-05 * * *
mercury -0.4584 0.161 -2.847 0.005086 * *
lead 1.031 0.19 5.427 2.524e-07 * * * 
## RMSE from cross-validation: 0.8902463
Variance Inflation Factors
  arsenic cadmium mercury lead
VIF 1.56 1.92 1.36 2.51

Density/metals
## FULL MODEL
## Adjusted R2 is: 0.49
Fitting linear model: N ~ arsenic + cadmium + chromium + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.1098 0.05881 -1.867 0.06407
arsenic -0.5128 0.1041 -4.926 2.398e-06 * * *
cadmium -0.7432 0.1297 -5.729 6.212e-08 * * *
chromium 0.2695 0.1071 2.517 0.01301 *
mercury -0.7299 0.132 -5.529 1.588e-07 * * *
lead 1.43 0.1592 8.986 1.907e-15 * * * 
## RMSE from cross-validation: 0.7065136
Variance Inflation Factors
  arsenic cadmium chromium mercury lead
VIF 1.56 2.08 1.75 1.43 2.7 

## REDUCED MODEL
## Adjusted R2 is: 0.49
Fitting linear model: N ~ arsenic + cadmium + chromium + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.1098 0.05881 -1.867 0.06407
arsenic -0.5128 0.1041 -4.926 2.398e-06 * * *
cadmium -0.7432 0.1297 -5.729 6.212e-08 * * *
chromium 0.2695 0.1071 2.517 0.01301 *
mercury -0.7299 0.132 -5.529 1.588e-07 * * *
lead 1.43 0.1592 8.986 1.907e-15 * * * 
## RMSE from cross-validation: 0.7065136
Variance Inflation Factors
  arsenic cadmium chromium mercury lead
VIF 1.56 2.08 1.75 1.43 2.7

Diversity/metals
## FULL MODEL
## Adjusted R2 is: 0.06
Fitting linear model: H ~ arsenic + cadmium + chromium + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.03115 0.07881 0.3952 0.6933
arsenic -0.115 0.1395 -0.8244 0.4112
cadmium -0.2287 0.1738 -1.316 0.1905
chromium -0.2563 0.1435 -1.786 0.0764
mercury 0.02354 0.1769 0.1331 0.8943
lead 0.2542 0.2133 1.192 0.2355 
## RMSE from cross-validation: 0.9303706
Variance Inflation Factors
  arsenic cadmium chromium mercury lead
VIF 1.56 2.08 1.75 1.43 2.7 

## REDUCED MODEL
## Adjusted R2 is: 0.07
Fitting linear model: H ~ chromium
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0391 0.077 0.5078 0.6124
chromium -0.2812 0.08139 -3.455 0.0007288 * * * 
## RMSE from cross-validation: 0.9157629
Variance Inflation Factors
  chromium
VIF 1

Evenness/metals
## FULL MODEL
## Adjusted R2 is: 0.03
Fitting linear model: J ~ arsenic + cadmium + chromium + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.07716 0.07843 0.9838 0.3269
arsenic 0.1621 0.1388 1.167 0.2451
cadmium 0.2536 0.173 1.466 0.145
chromium -0.238 0.1428 -1.666 0.09799
mercury 0.1657 0.176 0.9411 0.3483
lead -0.3184 0.2123 -1.5 0.1359 
## RMSE from cross-validation: 0.9265784
Variance Inflation Factors
  arsenic cadmium chromium mercury lead
VIF 1.56 2.08 1.75 1.43 2.7 

## REDUCED MODEL
## Adjusted R2 is: 0.02
Fitting linear model: J ~ chromium
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.05581 0.07726 0.7224 0.4713
chromium -0.1744 0.08166 -2.136 0.03445 * 
## RMSE from cross-validation: 0.9269077
Variance Inflation Factors
  chromium
VIF 1

1 mm community
Variable (or combination) S N H J
depth + - + +
om/silt
gravel -
sand - - -
clay - - -
Adjusted \(R^{2}\) 0.25 0.03 0.34 0.1
Variable (or combination) S N H J
arsenic
cadmium - -
chromium/iron/manganese
mercury
lead/copper/zinc + +
Adjusted \(R^{2}\) 0.08 0 0.05 0
Richness/habitat
## FULL MODEL
## Adjusted R2 is: 0.25
Fitting linear model: S ~ depth + om + gravel + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.04634 0.06256 -0.7407 0.4598
depth 0.2715 0.06482 4.188 4.307e-05 * * *
om -0.04458 0.1117 -0.3991 0.6903
gravel -0.1185 0.08561 -1.384 0.1679
sand -0.4367 0.1322 -3.304 0.001141 * *
clay -0.5032 0.1403 -3.586 0.0004272 * * * 
## RMSE from cross-validation: 0.8934522
Variance Inflation Factors
  depth om gravel sand clay
VIF 1.06 1.81 1.03 2.1 1.45 

## REDUCED MODEL
## Adjusted R2 is: 0.25
Fitting linear model: S ~ depth + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.03361 0.06184 -0.5436 0.5874
depth 0.2678 0.06475 4.136 5.298e-05 * * *
sand -0.3869 0.07612 -5.083 8.825e-07 * * *
clay -0.4563 0.114 -4.001 9.001e-05 * * * 
## RMSE from cross-validation: 0.8673699
Variance Inflation Factors
  depth sand clay
VIF 1.06 1.21 1.18

Density/habitat
## FULL MODEL
## Adjusted R2 is: 0.02
Fitting linear model: N ~ depth + om + gravel + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0211 0.07296 -0.2892 0.7727
depth -0.1713 0.07559 -2.266 0.02458 *
om -0.09757 0.1303 -0.749 0.4548
gravel 0.002914 0.09984 0.02919 0.9767
sand -0.29 0.1542 -1.881 0.06147
clay -0.3748 0.1636 -2.291 0.02309 * 
## RMSE from cross-validation: 1.008745
Variance Inflation Factors
  depth om gravel sand clay
VIF 1.06 1.81 1.03 2.1 1.45 

## REDUCED MODEL
## Adjusted R2 is: 0.03
Fitting linear model: N ~ depth + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.01888 0.07186 -0.2627 0.7931
depth -0.1719 0.07525 -2.285 0.02341 *
sand -0.1969 0.08846 -2.226 0.02718 *
clay -0.3066 0.1325 -2.314 0.02174 * 
## RMSE from cross-validation: 1.002598
Variance Inflation Factors
  depth sand clay
VIF 1.06 1.21 1.18

Diversity/habitat
## FULL MODEL
## Adjusted R2 is: 0.34
Fitting linear model: H ~ depth + om + gravel + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.02566 0.05866 -0.4374 0.6623
depth 0.4303 0.06078 7.079 2.765e-11 * * *
om 0.06608 0.1047 0.631 0.5288
gravel -0.1077 0.08027 -1.341 0.1815
sand -0.2444 0.124 -1.971 0.05013
clay -0.2757 0.1316 -2.096 0.03742 * 
## RMSE from cross-validation: 0.8438226
Variance Inflation Factors
  depth om gravel sand clay
VIF 1.06 1.81 1.03 2.1 1.45 

## REDUCED MODEL
## Adjusted R2 is: 0.34
Fitting linear model: H ~ depth + gravel + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.02847 0.0584 -0.4875 0.6265
depth 0.4311 0.06067 7.105 2.348e-11 * * *
gravel -0.119 0.0781 -1.524 0.1292
sand -0.3082 0.07143 -4.315 2.564e-05 * * *
clay -0.3236 0.1073 -3.017 0.002901 * * 
## RMSE from cross-validation: 0.8399062
Variance Inflation Factors
  depth gravel sand clay
VIF 1.06 1.01 1.21 1.18

Evenness/habitat
## FULL MODEL
## Adjusted R2 is: 0.08
Fitting linear model: J ~ depth + om + gravel + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.01566 0.06839 0.2289 0.8192
depth 0.2928 0.07086 4.133 5.383e-05 * * *
om 0.02954 0.1221 0.2419 0.8091
gravel 0.008159 0.09359 0.08718 0.9306
sand -0.04328 0.1445 -0.2995 0.7649
clay -0.02071 0.1534 -0.135 0.8927 
## RMSE from cross-validation: 0.9552492
Variance Inflation Factors
  depth om gravel sand clay
VIF 1.06 1.81 1.03 2.1 1.45 

## REDUCED MODEL
## Adjusted R2 is: 0.1
Fitting linear model: J ~ depth
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.01448 0.0664 0.2181 0.8276
depth 0.3126 0.06602 4.735 4.225e-06 * * * 
## RMSE from cross-validation: 0.9358617
Variance Inflation Factors
  depth
VIF 1

Richness/metals
## FULL MODEL
## Adjusted R2 is: 0.07
Fitting linear model: S ~ arsenic + cadmium + chromium + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.06323 0.08253 0.7661 0.4451
arsenic -0.1466 0.1335 -1.098 0.2742
cadmium -0.5481 0.1606 -3.413 0.0008755 * * *
chromium 0.01217 0.1638 0.07431 0.9409
mercury -0.001392 0.1293 -0.01077 0.9914
lead 0.4503 0.2727 1.651 0.1013 
## RMSE from cross-validation: 0.9298714
Variance Inflation Factors
  arsenic cadmium chromium mercury lead
VIF 1.65 1.85 1.89 1.48 3.17 

## REDUCED MODEL
## Adjusted R2 is: 0.08
Fitting linear model: S ~ cadmium + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.06172 0.08196 0.7531 0.4529
cadmium -0.5082 0.149 -3.409 0.000881 * * *
lead 0.3104 0.1476 2.103 0.03751 * 
## RMSE from cross-validation: 0.9219954
Variance Inflation Factors
  cadmium lead
VIF 1.73 1.73

Density/metals
## FULL MODEL
## Adjusted R2 is: -0.01
Fitting linear model: N ~ arsenic + cadmium + chromium + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.01452 0.09066 0.1602 0.873
arsenic -0.1569 0.1467 -1.07 0.287
cadmium -0.161 0.1764 -0.9126 0.3633
chromium -0.2011 0.18 -1.117 0.2661
mercury -0.1079 0.142 -0.7597 0.4489
lead 0.4704 0.2995 1.57 0.1189 
## RMSE from cross-validation: 1.030112
Variance Inflation Factors
  arsenic cadmium chromium mercury lead
VIF 1.65 1.85 1.89 1.48 3.17 

## REDUCED MODEL
## Adjusted R2 is: 0
Fitting linear model: N ~ 1
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.01849 0.08981 0.2058 0.8373 
## RMSE from cross-validation: 1.011527

Quitting from lines 675-677 (C1_analyses_B.Rmd) Erreur dans Qr$qr[p1, p1, drop = FALSE] : indice hors limites De plus : Il y a eu 35 avis (utilisez warnings() pour les visionner)

Diversity/metals
## FULL MODEL
## Adjusted R2 is: 0.04
Fitting linear model: H ~ arsenic + cadmium + chromium + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.09394 0.08016 1.172 0.2435
arsenic -0.1326 0.1297 -1.023 0.3086
cadmium -0.4559 0.156 -2.923 0.004141 * *
chromium 0.006562 0.1591 0.04124 0.9672
mercury 0.03018 0.1255 0.2404 0.8104
lead 0.3954 0.2648 1.493 0.1381 
## RMSE from cross-validation: 0.8995954
Variance Inflation Factors
  arsenic cadmium chromium mercury lead
VIF 1.65 1.85 1.89 1.48 3.17 

## REDUCED MODEL
## Adjusted R2 is: 0.05
Fitting linear model: H ~ cadmium + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.09219 0.07962 1.158 0.2492
cadmium -0.4268 0.1448 -2.948 0.003831 * *
lead 0.2923 0.1434 2.038 0.04365 * 
## RMSE from cross-validation: 0.8942355
Variance Inflation Factors
  cadmium lead
VIF 1.73 1.73

Evenness/metals
## FULL MODEL
## Adjusted R2 is: -0.04
Fitting linear model: J ~ arsenic + cadmium + chromium + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.06202 0.07961 0.779 0.4375
arsenic -0.05693 0.1288 -0.4421 0.6592
cadmium -0.04309 0.1549 -0.2782 0.7813
chromium 0.05701 0.158 0.3607 0.7189
mercury 0.03799 0.1247 0.3047 0.7611
lead -0.004916 0.263 -0.01869 0.9851 
## RMSE from cross-validation: 0.8928066
Variance Inflation Factors
  arsenic cadmium chromium mercury lead
VIF 1.65 1.85 1.89 1.48 3.17 

## REDUCED MODEL
## Adjusted R2 is: 0
Fitting linear model: J ~ 1
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.06244 0.07799 0.8006 0.4249 
## RMSE from cross-validation: 0.873627

Quitting from lines 699-701 (C1_analyses_B.Rmd) Erreur dans Qr\(qr[p1, p1, drop = FALSE] : indice hors limites De plus : Messages d'avis : 1: Dans CVlm(data = lm_out\)model, form.lm = lm_out, m = 5, printit = F) :

As there is >1 explanatory variable, cross-validation predicted values for a fold are not a linear function of corresponding overall predicted values. Lines that are shown for the different folds are approximate

2: Dans CVlm(data = lm_out$model, form.lm = lm_out, m = 5, printit = F) :

As there is >1 explanatory variable, cross-validation predicted values for a fold are not a linear function of corresponding overall predicted values. Lines that are shown for the different folds are approximate

3: Dans CVlm(data = lm_out$model, form.lm = lm_out, m = 5, printit = F) :

As there is >1 explanatory variable, cross-validation predicted values for a fold are not a linear function of corresponding overall predicted values. Lines that are shown for the different folds are approximate

4: Dans CVlm(data = lm_out$model, form.lm = lm_out, m = 5, printit = F) :

As there is >1 explanatory variable, cross-validation predicted values for a fold are not a linear function of corresponding overall predicted values. Lines that are shown for the different folds are approximate

2.3. Multivariate regression

Independant variables are habitat parameters or heavy metal concentrations, dependant variables are species abundances for each community. Variables have been standardized by mean and standard-deviation, and outliers and correlated variables have been excluded. Taxon densities were (log+1) transformed.

This analysis has been done on PRIMER, with a DistLM to identify the variables that explain the most the community variability and with a dbRDA to plot the results.

0.5 mm community
Habitat

Variables selected by the DistLM procedure have a \(R^{2}\) of 0.27.

Metals

Variables selected by the DistLM procedure have a \(R^{2}\) of 0.18.

1 mm community
Habitat

Variables selected by the DistLM procedure have a \(R^{2}\) of 0.14.

Metals

Variables selected by the DistLM procedure have a \(R^{2}\) of 0.07.

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